Gajanan Balasubramaniam

Collaborated on a modernization initiative for financial services by transitioning and optimizing existing data processes to DBT, Airflow, GitHub Actions, and Terraform, enhancing efficiency and reliability. Assisted a large Canadian grocery retailer to design and implement a high-performance recommendation engine backend using Terraform, GitLabs, ClickHouse, and Python, deployed with Kubernetes.

Led the automation and refinement of GIS workflows using QGIS, increasing efficiency and streamlining processes. Oversaw comprehensive data management tasks, produced intricate mapping visuals, and conducted in-depth spatial data analyses to drive informed decision-making. Engaged with top-tier telecommunication providers, offering insights and guidance rooted in current policy, ensuring alignment

Developed a location tracking system for bus operations using Python, SQL, RabbitMQ, Geoevent Server, ArcGIS Server, and REST APIs, saving 30 hours weekly. Designed an ETL integration tool for COVID-19 PPE supply chain processes using Python, Pandas, SQL, SFTP, and XML, saving 4 work hours daily and ~$25000 monthly. Orchestrated and developed a Workblock tool to map unavailable tracks during maint

Implemented a web scraping tool for gathering land development information using Python, Pandas, Selenium, Docker, GIT, and AWS (RDS, S3), delivering readily accessible datasets and increasing decision-making by 10%. Engineered an end-to-end data processing solution on AWS leveraging PySpark on an EMR cluster, ingesting raw data from S3, applying transformations, and storing optimized output in Pa

Played various roles during the 2016 WildFire in Alberta as part of the Response and Recovery team, leveraging Python, SQL Server, AWS, ArcGIS Server, JavaScript, HTML, CSS, and Power BI to help first responders. Coordinated with the Fire Department in architecting a data visualization dashboard with cutting-edge geospatial processing and visualization, improving fire department efficiency for res

Converted City of Toronto’s MicroStation datasets to Esri-compatible geospatial formats using Python and FME, integrating with ArcGIS Desktop/Server for automated GIS workflows. Developed robust Python scripts leveraging GDAL and OpenCV to process satellite imagery, extract vegetation indices, and generate vectorized outputs. Engineered custom spatial data pipelines using ArcPy and geopandas for a

Pursuing the Google Professional Machine Learning Engineer Certification, expected in February 2025, focusing on designing, building, and productionizing ML models.

Obtained the Google Cloud Certified Professional Data Engineer certification in October 2022, demonstrating expertise in designing and building data processing systems on Google Cloud Platform.

Achieved the Google Professional Cloud Architect Certification in April 2024, validating advanced skills in designing, developing, and managing robust, secure, scalable, highly available, and dynamic solutions to drive business objectives.

Earned the Google Professional Cloud Database Engineer Certification in February 2024, showcasing proficiency in designing, implementing, managing, and troubleshooting Google Cloud database solutions.

Obtained the Terraform Associate 003 Certification in February 2025, demonstrating foundational knowledge and skills in infrastructure as code using Terraform.

Achieved Apache Airflow Fundamentals Certified in October 2022, validating core understanding and practical skills in orchestrating data pipelines with Apache Airflow.

Completed a Bachelor of Environmental Studies with a specialization in Geomatics and Computer Science. This program provided a strong foundation in spatial data analysis, environmental management, and computer programming principles.